mirror of https://github.com/llvm/torch-mlir
43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
# -*- Python -*-
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# This file is licensed under a pytorch-style license
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# See frontends/pytorch/LICENSE for license information.
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import typing
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import torch
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import torch_mlir
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# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
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mb = torch_mlir.ModuleBuilder()
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class TestModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear = torch.nn.quantized.Linear(5, 2, dtype=torch.qint8)
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self.linear_no_bias = torch.nn.quantized.Linear(6,
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2,
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bias_=False,
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dtype=torch.qint8)
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# CHECK: %[[SCALE:.*]] = torch.constant.float
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# CHECK: %[[ZERO_POINT:.*]] = torch.constant.int 0 : i64
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# CHECK: %[[INT_REPR:.*]] = torch.tensor({{.*}}) : !torch.tensor<[2,5],si8>
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# CHECK: %[[WEIGHTS:.*]] = torch.per_tensor_affine.create %[[INT_REPR]], %[[SCALE]], %[[ZERO_POINT]] : !torch.tensor<[2,5],si8>, f64, i64 -> !torch.tensor<[2,5],!torch.qint8>
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# CHECK: %[[BIAS:.*]] = torch.tensor({{.*}}) : !torch.tensor<[2],f32>
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# CHECK: %[[LINEAR_PARAMS:.*]] = torch.linear_params.create %[[WEIGHTS]], %[[BIAS]] : !torch.tensor<[2,5],!torch.qint8>, !torch.tensor<[2],f32>
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@torch.jit.export
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def test_linear(self, t):
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return self.linear(t)
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# CHECK: %[[LINEAR_PARAMS_NO_BIAS:.*]] = torch.linear_params.create %{{[^,]*}} : !torch.tensor<[2,6],!torch.qint8>
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@torch.jit.export
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def test_linear_no_bias(self, t):
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return self.linear_no_bias(t)
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test_module = TestModule()
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recursivescriptmodule = torch.jit.script(test_module)
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# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
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mb.import_module(recursivescriptmodule._c)
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mb.module.operation.print()
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